{"id":"W4315629645","doi":"10.1109/globecom48099.2022.10001116","title":"Optimizing Information Freshness Leveraging Multi-RISs in NOMA-based IoT Networks","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Age of Information Optimization","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Base station; Telecommunications link; Cluster analysis; Optimization problem; Wireless; Relaxation (psychology); Transmitter power output; Wireless sensor network; Mathematical optimization; Upload; Codebook; Computer network; Distributed computing; Algorithm; Channel (broadcasting); Telecommunications; Transmitter; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001553111,0.001392944,0.001274754,0.000543079,0.0007474124,0.001494645,0.001596805,0.001013834,0.001103478],"category_scores_gemma":[0.003370171,0.000636749,0.0006504302,0.001048636,0.001201585,0.001823624,0.00157398,0.001040476,0.0002785781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001033137,"about_ca_system_score_gemma":0.001111868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003328361,"about_ca_topic_score_gemma":0.004243738,"domain_scores_codex":[0.9990892,0.0003141054,0.00003750116,0.0001887693,0.0001684019,0.0002019638],"domain_scores_gemma":[0.998233,0.001079855,0.0002487988,0.0001376115,0.0001883445,0.0001123648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000825802,0.00003979251,0.0003890994,0.0000640846,0.00003378529,0.0001214185,0.00005547113,0.9749834,0.002281161,0.006128137,0.0006289139,0.01519211],"study_design_scores_gemma":[0.000003986496,0.0000372598,0.00009918311,0.000004184989,0.000009284549,0.0000240303,0.00002104286,0.9968353,0.0003799043,0.002373411,0.0002060886,0.000006218039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05817337,0.001074981,0.9364483,0.0003226718,0.0001159828,0.00005588364,0.00009166538,0.0002539608,0.003463231],"genre_scores_gemma":[0.9455596,0.0004301507,0.05202692,0.0001299642,0.00005657118,0.00006495494,0.00008948176,0.00004514022,0.001597173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003328361,"threshold_uncertainty_score":0.008213758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03479680152394302,"score_gpt":0.2695380746530084,"score_spread":0.2347412731290654,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}